code: def test(
      self, test_case_description, params, input, features, expected
  ):
      # Create input tensors
      input_tensor = torch.from_numpy(np.array(input)).to(
          dtype=torch.float, device=torch.device("cuda")
      )
      feature_tensor = torch.from_numpy(np.array(features)).to(
          dtype=torch.float, device=torch.device("cuda")
      )
  
      params[-1] = None if params[-1] is None else params[-1].cuda()
  
      # apply filter
      crf = CRF(*params)
      output = crf(input_tensor, feature_tensor).cpu().numpy()
  
      # Ensure result are as expected
      # np.testing.assert_allclose(output, expected, atol=1e-4)
  
      # Temporarily allowing some (10%) mismatched elements due to non determinism.
      absolute_diff_tolerance = 5e-2
      mismatch_ratio_tolerance = 0.1
  
      output = np.array(output).flatten()
      expected = np.array(expected).flatten()
  
      abs_diff = abs(output - expected)
      mismatch_count = sum(
          np.where(abs_diff > absolute_diff_tolerance, 1, 0)
      )
  
      self.assertLessEqual(
          mismatch_count / len(output), mismatch_ratio_tolerance
      )
  
file_path: MONAI/tests/test_crf_cuda.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai.networks.blocks import CRF
  from tests.utils import skip_if_no_cpp_extension, skip_if_no_cuda
label_desc: Use image node_id 1 for calling the CRFTestCaseCuda obj's underlying member method code with example usage: obj.test(test_case_description, params, input, features, expected) without return types